The present disclosure relates to technology for modeling unique identification codes in virtual interaction context using natural language processing.
Providing online users with recommendations relevant to items that they show interest in facilitates an online interaction process and improves the experience of the users of an online service. However, there is a large number of user activities and transactions (thousands, millions, etc.) performed on a recommendation system every hour. Modeling relevant or similar products is therefore particularly challenging because of the amount of data needed to be processed. Thus, there is a need to process large volumes of data efficiently, and to recommend product based on unique identification codes that are actually relevant to online users.
On the other hand, for new users, very little historical data about that user may be available to generate effective item recommendations during a user visit to an associated website. Thus, performing data modeling on the unique identification codes, such as stock keeping units, directly can result in sparse data problems, and, as a result, current systems are ineffective at identifying good latent representations that can allow models to share information between similar unique identification codes for operations such as interaction prediction, recommendations, item clustering, etc.